TECHNICAL MEMORANDUM                                          TM-26-005

SUBJECT:      What seven weeks of AI-assisted development actually cost
DATE:         2026-07-18


SUMMARY. For seven weeks, one developer (me) paired with an AI coding
agent to build a product: 54 delivered work items in the three most
intensive weeks, at roughly USD 36 each. The surprise is where the
money went: 65% of spend was the agent re-reading its own conversation
history. Session hygiene, not model choice, is the cost lever.

1. BACKGROUND. Three of us are building a bootstrapped product on the
side: five repositories, first commit to production deployment in
seven weeks, most of the implementation done by an AI coding agent.
The agent's tooling records every API request, so I ran the numbers
for my own pairing with it: token-level costs for 69 working
sessions, converted to API list prices. I then cross-checked the
pattern on two smaller side projects of very different shape - a
hardware hobby project and a documentation project.

2. FINDINGS.

   2.1 Output first: 635 commits across the five repositories. In the
   three-week window with full token data, 54 delivered work items;
   in the deployment repository the agent co-authored 97% of commits.

   2.2 The bill for those three weeks, at list prices: about
   USD 1,921. That is USD 36 per delivered work item, USD 3 per
   commit, USD 1.64 per prompt I typed.

   2.3 Where it went: 65% of spend was cache reads - the agent
   re-reading the session's accumulated context on every request.
   Freshly typed input was near 0%, the model's own output 13%. Both
   side projects showed the same split, with cache reads at 60–65%.

   2.4 The expensive pattern is the immortal session: kept open for
   days as a workspace. Twelve such sessions consumed 59% of all
   spend. The worst began as one small question and ran three days,
   133 prompts, USD 400. A marginal prompt in a mature session costs
   USD 2–3; in a fresh one, about USD 0.50 - however simple the
   question.

   2.5 The cheap patterns: sessions scoped to a single task averaged
   USD 0.56 per prompt, four times cheaper than the long-lived ones.
   All exploration delegated to subagents together cost 2% of total.

3. ASSUMPTIONS. All dollar figures are API list-price equivalents
computed from recorded token usage; the account runs on a flat
subscription, which bills differently. Read them as a resource-
intensity metric: the relative numbers are the lesson. One
developer's practice, three projects, consistent direction.

4. CONCLUSIONS. Cost is context size times request count; everything
else is detail. The fix is not a cheaper model but a working
discipline: one session per task, and a fresh session when the topic
changes. That is only cheap if the durable state lives outside the
conversation - in plan documents, tickets, and decision ledgers a new
session can re-read in seconds. Documentation discipline turns out to
be the actual cost optimization. Each model generation raises the
price of bad session habits.

5. ACTION. The working rules this yields: new topic, new session.
Treat context compaction as a smell that one session should have been
two. Delegate exploration to subagents. Script anything the agent
does more than a few times. Keep decisions in files, not in the
conversation.